Federico Sau

How do various maize crop models vary in their responses to climate change factors

Potential consequences of climate change on crop production can be studied using mechanistic crop simulation models. While a broad variety of maize simulation models exist, it is not known whether different models diverge on grain yield responses to changes in climatic factors, or whether they agree in their general trends related to phenology, growth, and yield. With the goal of analyzing the sensitivity of simulated yields to changes in temperature and atmospheric carbon dioxide concentrations [CO2 ], we present the largest maize crop model intercomparison to date, including 23 different models. These models were evaluated for four locations representing a wide range of maize production conditions in the world: Lusignan (France), Ames (USA), Rio Verde (Brazil) and Morogoro (Tanzania). While individual models differed considerably in absolute yield simulation at the four sites, an ensemble of a minimum number of models was able to simulate absolute yields accurately at the four sites even with low data for calibration, thus suggesting that using an ensemble of models has merit. Temperature increase had strong negative influence on modeled yield response of roughly -0.5 Mg ha(-1) per °C. Doubling [CO2 ] from 360 to 720 μmol mol(-1) increased grain yield by 7.5% on average across models and the sites. That would therefore make temperature the main factor altering maize yields at the end of this century. Furthermore, there was a large uncertainty in the yield response to [CO2 ] among models. Model responses to temperature and [CO2 ] did not differ whether models were simulated with low calibration information or, simulated with high level of calibration information.

Calibration and use of CROPGRO-soybean model for improving soybean management under rainfed conditions

Abstract Crops such as soybean ( Glycine max L.) are grown predominantly under rainfed conditions where water is a major limiting factor and the interannual variability in rainfall pattern is high. Crop modeling has proven a valuable tool to evaluate the long-term consequences of weather patterns, but the candidate crop models must be tested and calibrated for new regions prior to their use as extrapolation tools to predict optimum cultivar choice and sowing dates. The objectives of this paper were to calibrate the CROPGRO-soybean model for growth and yield under rainfed conditions in Galicia, northwest Spain, and then to use the calibrated model to establish the best sowing dates for three cultivars at three locations in this region. The starting point of the calibration process was the CROPGRO-soybean version previously calibrated for non-limiting water conditions. The original model, when simulated versus rainfed soybean field data sets, tended to simulate more severe water stress than actually occurred. In order to calibrate growth and yield for the actual soil we tried several ways for the modelled crop to have access to more water. Modifications were made on soil depth, water holding capacity, and root elongation rate. In addition, other changes were made to predict accurately the observed water-stress induced acceleration of maturity. Long-term simulations with recorded weather data showed that soybean is more sensitive to planting date under irrigated than rainfed management, in the three studied Galician locations.

Evaluation and improvement of CROPGRO-soybean model for a cool environment in Galicia, northwest Spain

Abstract This paper describes the evaluation and improvement of the CROPGRO-soybean model for a cool environment in northwest Spain. The model was evaluated with a soybean (Glycine max L.) 1994 field data set with three cultivars and three planting dates. The original model proved to underestimate biomass and seed yield, so modifications were made to the temperature functions affecting N2 fixation and photosynthesis, in order to fit better to the experimental data. The modified model was then tested, i.e., validated, with independent experimental data collected in 1995 at the same site with the same cultivars and four planting dates. Comparing observed with simulated data in 1994, the modified model decreased the root mean square error (RMSE) for biomass at harvest and seed yield from 1714 to 466 and from 940 to 333, respectively. For 1995, the validation year, RMSE for biomass decreased from 1366 to 352, although the yield was now overestimated with no significant change in RMSE. The average simulated harvest index (HI) for 1995 was greater than for 1994, the reverse of the measured values. Nevertheless, the modified model was more reliable in predicting crop performances under these cool conditions than the original model. Moreover, the predictions of the modified model for a warm climate (Gainesville, FL) were quite acceptable. We conclude that the modified model can be used successfully over a wider range of climates than the original version.